Numpy MaskedArray.masked_invalid() function | Python (original) (raw)

Last Updated : 27 Sep, 2019

In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arrays that may have missing or invalid entries.

numpy.MaskedArray.masked_invalid() function is used to mask an array where invalid values occur (NaNs or infs).This function is a shortcut to masked_where, with condition = ~(numpy.isfinite(arr)).

Syntax : numpy.ma.masked_invalid(arr, copy=True)

Parameters:
arr : [ndarray] Input array which we want to mask.
copy : [bool] If True (default) make a copy of arr in the result. If False modify arr in place and return a view.

Return : [ MaskedArray] The resultant array after masking.

Code #1 :

import numpy as geek

import numpy.ma as ma

in_arr = geek.array([ 1 , 2 , geek.nan, - 1 , geek.inf])

print ( "Input array : " , in_arr)

mask_arr = ma.masked_invalid(in_arr)

print ( "Masked array : " , mask_arr)

Output:

Input array : [ 1. 2. nan -1. inf] Masked array : [1.0 2.0 -- -1.0 --]

Code #2 :